Memristors, also known as resistive memory devices, are two terminal devices whose resistance values depend on an internal state variable and can be modulated by the history of external stimulation. Unlike conventional charge-based electronic devices, a memristor's state is determined by the internal ion (either cation or anion) configuration, where the re-distribution of oxygen ions or metal cations inside the device modulates the local resistivity and overall device resistance. Memristors have been extensively studied for both digital memory and analog logic circuit applications. At the device level, memristors have been shown to be able to emulate synaptic functions by storing the analog synaptic weights and implementing synaptic learning rules.
When constructed into a crossbar form, memristor networks offer the desired density and connectivity that are required for hardware implementation of neuromorphic computing systems. Recently, memristor arrays and phase change memory devices have been used as artificial neural networks to perform pattern classification tasks. Other studies have shown memristors can be employed in recurrent artificial neural networks for applications such as analog to digital convertors. Memristor-based architectures have also been proposed and analyzed for tasks such as sparse coding and dictionary learning. The ability to sparsely encode data is believed to be a key mechanism by which biological neural systems can efficiently process complex, large amount of sensory data, and can enable the implementation of efficient bio-inspired neuromorphic systems for data representation and analysis.
In this disclosure, the implementation of a sparse coding algorithm is demonstrated in a memristor crossbar, and shown that this network can be used to perform applications such as natural image analysis using learned dictionaries.
This section provides background information related to the present disclosure which is not necessarily prior art.